Predict if a car purchased at auction is a lemon
Data Source and Introduction
Name:
Don’t Get Kicked!!
From:
Kaggle Competition
Introduction:
This data set was published by Carvana at www.kaggle.com, which refers to the considerations a buyer has to make in order to buy a used car that’s in the best possible conditions.
Source:
Carvana
URL:
https://www.kaggle.com/c/DontGetKicked/data.
##Variable Description
Independent Variables
kable(vars[-4, ], table.attr='class="table-fixed-header"', row.names = F) %>%
kable_styling(full_width=F)| Field.Name | Variable.Type | Definition |
|---|---|---|
| PurchDate | Continuous | The Date the vehicle was Purchased at Auction |
| VehicleAge | Continuous | The Years elapsed since the manufacturer’s year |
| VehOdo | Continuous | The vehicles odometer reading |
| MMRAcquisitionAuctionCleanPrice | Continuous | Acquisition price for this vehicle in the above Average condition at time of purchase |
| MMRAcquisitionRetailAveragePrice | Continuous | Acquisition price for this vehicle in the retail market in average condition at time of purchase |
| MMRAcquisitionRetailCleanPrice | Continuous | Acquisition price for this vehicle in the retail market in above average condition at time of purchase |
| MMRCurrentAuctionAveragePrice | Continuous | Acquisition price for this vehicle in average condition as of current day |
| MMRCurrentAuctionCleanPrice | Continuous | Acquisition price for this vehicle in the above condition as of current day |
| MMRCurrentRetailAveragePrice | Continuous | Acquisition price for this vehicle in the retail market in average condition as of current day |
| MMRCurrentRetailCleanPrice | Continuous | Acquisition price for this vehicle in the retail market in above average condition as of current day |
| VNZIP | Continuous | Zipcode where the car was purchased |
| VehBCost | Continuous | Acquisition cost paid for the vehicle at time of purchase |
| WarrantyCost | Continuous | Warranty price (term=36month and millage=36K) |
| RefID | Category | Unique (sequential) number assigned to vehicles |
| IsBadBuy | Category | Identifies if the kicked vehicle was an avoidable purchase |
| Auction | Category | Auction provider at which the vehicle was purchased |
| VehYear | Category | The manufacturer’s year of the vehicle |
| Make | Category | Vehicle Manufacturer |
| Model | Category | Vehicle Model |
| Trim | Category | Vehicle Trim Level |
| SubModel | Category | Vehicle Submodel |
| Color | Category | Vehicle Color |
| Transmission | Category | Vehicles transmission type (Automatic, Manual) |
| WheelTypeID | Category | The type id of the vehicle wheel |
| WheelType | Category | The vehicle wheel type description (Alloy, Covers) |
| Nationality | Category | The Manufacturer’s country |
| Size | Category | The size category of the vehicle (Compact, SUV, etc.) |
| TopThreeAmericanName | Category | Identifies if the manufacturer is one of the top three American manufacturers |
| PRIMEUNIT | Category | Identifies if the vehicle would have a higher demand than a standard purchase |
| AUCGUART | Category | The level guarntee provided by auction for the vehicle (Green light - Guaranteed/arbitratable, Yellow Light - caution/issue, red light - sold as is) |
| BYRNO | Category | Unique number assigned to the buyer that purchased the vehicle |
| VNST | Category | State where the the car was purchased |
| IsOnlineSale | Category | Identifies if the vehicle was originally purchased online |
Dependent Variables
kable(vars[4, ], table.attr='class="table-fixed-header"', row.names=F) %>%
kable_styling(position="left", full_width=T)| Field.Name | Variable.Type | Definition |
|---|---|---|
| MMRAcquisitionAuctionAveragePrice | Continuous | Acquisition price for this vehicle in average condition at time of purchase |
Data Appearance
kable(head(cars), table.attr='class="table-fixed-header"') %>%
kable_styling() %>%
scroll_box(height=F)| RefId | IsBadBuy | PurchDate | Auction | VehYear | VehicleAge | Make | Model | Trim | SubModel | Color | Transmission | WheelTypeID | WheelType | VehOdo | Nationality | Size | TopThreeAmericanName | MMRAcquisitionAuctionAveragePrice | MMRAcquisitionAuctionCleanPrice | MMRAcquisitionRetailAveragePrice | MMRAcquisitionRetailCleanPrice | MMRCurrentAuctionAveragePrice | MMRCurrentAuctionCleanPrice | MMRCurrentRetailAveragePrice | MMRCurrentRetailCleanPrice | PRIMEUNIT | AUCGUART | BYRNO | VNZIP | VNST | VehBCost | IsOnlineSale | WarrantyCost |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 12/7/2009 | ADESA | 2006 | 3 | MAZDA | MAZDA3 | i | 4D SEDAN I | RED | AUTO | 1 | Alloy | 89046 | OTHER ASIAN | MEDIUM | OTHER | 8155 | 9829 | 11636 | 13600 | 7451 | 8552 | 11597 | 12409 | NULL | NULL | 21973 | 33619 | FL | 7100 | 0 | 1113 |
| 2 | 0 | 12/7/2009 | ADESA | 2004 | 5 | DODGE | 1500 RAM PICKUP 2WD | ST | QUAD CAB 4.7L SLT | WHITE | AUTO | 1 | Alloy | 93593 | AMERICAN | LARGE TRUCK | CHRYSLER | 6854 | 8383 | 10897 | 12572 | 7456 | 9222 | 11374 | 12791 | NULL | NULL | 19638 | 33619 | FL | 7600 | 0 | 1053 |
| 3 | 0 | 12/7/2009 | ADESA | 2005 | 4 | DODGE | STRATUS V6 | SXT | 4D SEDAN SXT FFV | MAROON | AUTO | 2 | Covers | 73807 | AMERICAN | MEDIUM | CHRYSLER | 3202 | 4760 | 6943 | 8457 | 4035 | 5557 | 7146 | 8702 | NULL | NULL | 19638 | 33619 | FL | 4900 | 0 | 1389 |
| 4 | 0 | 12/7/2009 | ADESA | 2004 | 5 | DODGE | NEON | SXT | 4D SEDAN | SILVER | AUTO | 1 | Alloy | 65617 | AMERICAN | COMPACT | CHRYSLER | 1893 | 2675 | 4658 | 5690 | 1844 | 2646 | 4375 | 5518 | NULL | NULL | 19638 | 33619 | FL | 4100 | 0 | 630 |
| 5 | 0 | 12/7/2009 | ADESA | 2005 | 4 | FORD | FOCUS | ZX3 | 2D COUPE ZX3 | SILVER | MANUAL | 2 | Covers | 69367 | AMERICAN | COMPACT | FORD | 3913 | 5054 | 7723 | 8707 | 3247 | 4384 | 6739 | 7911 | NULL | NULL | 19638 | 33619 | FL | 4000 | 0 | 1020 |
| 6 | 0 | 12/7/2009 | ADESA | 2004 | 5 | MITSUBISHI | GALANT 4C | ES | 4D SEDAN ES | WHITE | AUTO | 2 | Covers | 81054 | OTHER ASIAN | MEDIUM | OTHER | 3901 | 4908 | 6706 | 8577 | 4709 | 5827 | 8149 | 9451 | NULL | NULL | 19638 | 33619 | FL | 5600 | 0 | 594 |
Summary of Variable
Dependent Variable
dep <- as.data.frame(table(IsBadBuy))
kable(dep, table.attr='class="table-fixed-header"', align="l") %>%
kable_styling(full_width=F, position="float_left")| IsBadBuy | Freq |
|---|---|
| 0 | 64007 |
| 1 | 8976 |
Tables of Categorical Variables
Categorical
| 0 (n=64007) |
1 (n=8976) |
Total (n=72983) |
|
|---|---|---|---|
| Auction | |||
| ADESA | 12246 (19.1%) | 2193 (24.4%) | 14439 (19.8%) |
| MANHEIM | 36328 (56.8%) | 4715 (52.5%) | 41043 (56.2%) |
| OTHER | 15433 (24.1%) | 2068 (23.0%) | 17501 (24.0%) |
| VehYear | |||
| 2001 | 1055 (1.6%) | 426 (4.7%) | 1481 (2.0%) |
| 2002 | 2587 (4.0%) | 818 (9.1%) | 3405 (4.7%) |
| 2003 | 5017 (7.8%) | 1210 (13.5%) | 6227 (8.5%) |
| 2004 | 8620 (13.5%) | 1587 (17.7%) | 10207 (14.0%) |
| 2005 | 13457 (21.0%) | 2032 (22.6%) | 15489 (21.2%) |
| 2006 | 15443 (24.1%) | 1600 (17.8%) | 17043 (23.4%) |
| 2007 | 10535 (16.5%) | 888 (9.9%) | 11423 (15.7%) |
| 2008 | 6500 (10.2%) | 385 (4.3%) | 6885 (9.4%) |
| 2009 | 792 (1.2%) | 30 (0.3%) | 822 (1.1%) |
| 2010 | 1 (0.0%) | 0 (0%) | 1 (0.0%) |
| VehicleAge | |||
| 0 | 2 (0.0%) | 0 (0%) | 2 (0.0%) |
| 1 | 2969 (4.6%) | 125 (1.4%) | 3094 (4.2%) |
| 2 | 7942 (12.4%) | 540 (6.0%) | 8482 (11.6%) |
| 3 | 14601 (22.8%) | 1301 (14.5%) | 15902 (21.8%) |
| 4 | 15149 (23.7%) | 1864 (20.8%) | 17013 (23.3%) |
| 5 | 11061 (17.3%) | 1895 (21.1%) | 12956 (17.8%) |
| 6 | 6575 (10.3%) | 1447 (16.1%) | 8022 (11.0%) |
| 7 | 3641 (5.7%) | 1005 (11.2%) | 4646 (6.4%) |
| 8 | 1623 (2.5%) | 597 (6.7%) | 2220 (3.0%) |
| 9 | 444 (0.7%) | 202 (2.3%) | 646 (0.9%) |
| Make | |||
| ACURA | 24 (0.0%) | 9 (0.1%) | 33 (0.0%) |
| BUICK | 607 (0.9%) | 113 (1.3%) | 720 (1.0%) |
| CADILLAC | 28 (0.0%) | 5 (0.1%) | 33 (0.0%) |
| CHEVROLET | 15567 (24.3%) | 1681 (18.7%) | 17248 (23.6%) |
| CHRYSLER | 7707 (12.0%) | 1137 (12.7%) | 8844 (12.1%) |
| DODGE | 11579 (18.1%) | 1333 (14.9%) | 12912 (17.7%) |
| FORD | 9563 (14.9%) | 1742 (19.4%) | 11305 (15.5%) |
| GMC | 574 (0.9%) | 75 (0.8%) | 649 (0.9%) |
| HONDA | 443 (0.7%) | 54 (0.6%) | 497 (0.7%) |
| HUMMER | 1 (0.0%) | 0 (0%) | 1 (0.0%) |
| HYUNDAI | 1578 (2.5%) | 233 (2.6%) | 1811 (2.5%) |
| INFINITI | 28 (0.0%) | 14 (0.2%) | 42 (0.1%) |
| ISUZU | 125 (0.2%) | 9 (0.1%) | 134 (0.2%) |
| JEEP | 1390 (2.2%) | 254 (2.8%) | 1644 (2.3%) |
| KIA | 2192 (3.4%) | 292 (3.3%) | 2484 (3.4%) |
| LEXUS | 20 (0.0%) | 11 (0.1%) | 31 (0.0%) |
| LINCOLN | 68 (0.1%) | 29 (0.3%) | 97 (0.1%) |
| MAZDA | 821 (1.3%) | 158 (1.8%) | 979 (1.3%) |
| MERCURY | 758 (1.2%) | 155 (1.7%) | 913 (1.3%) |
| MINI | 16 (0.0%) | 8 (0.1%) | 24 (0.0%) |
| MITSUBISHI | 907 (1.4%) | 123 (1.4%) | 1030 (1.4%) |
| NISSAN | 1752 (2.7%) | 333 (3.7%) | 2085 (2.9%) |
| OLDSMOBILE | 194 (0.3%) | 49 (0.5%) | 243 (0.3%) |
| PLYMOUTH | 1 (0.0%) | 1 (0.0%) | 2 (0.0%) |
| PONTIAC | 3751 (5.9%) | 507 (5.6%) | 4258 (5.8%) |
| SATURN | 1857 (2.9%) | 306 (3.4%) | 2163 (3.0%) |
| SCION | 118 (0.2%) | 11 (0.1%) | 129 (0.2%) |
| SUBARU | 22 (0.0%) | 6 (0.1%) | 28 (0.0%) |
| SUZUKI | 1133 (1.8%) | 195 (2.2%) | 1328 (1.8%) |
| TOYOTA | 1030 (1.6%) | 114 (1.3%) | 1144 (1.6%) |
| TOYOTA SCION | 1 (0.0%) | 0 (0%) | 1 (0.0%) |
| VOLKSWAGEN | 115 (0.2%) | 19 (0.2%) | 134 (0.2%) |
| VOLVO | 37 (0.1%) | 0 (0%) | 37 (0.1%) |
| Color | |||
| BEIGE | 1373 (2.1%) | 211 (2.4%) | 1584 (2.2%) |
| BLACK | 6769 (10.6%) | 858 (9.6%) | 7627 (10.5%) |
| BLUE | 9158 (14.3%) | 1189 (13.2%) | 10347 (14.2%) |
| BROWN | 380 (0.6%) | 56 (0.6%) | 436 (0.6%) |
| GOLD | 4494 (7.0%) | 737 (8.2%) | 5231 (7.2%) |
| GREEN | 2792 (4.4%) | 402 (4.5%) | 3194 (4.4%) |
| GREY | 6976 (10.9%) | 911 (10.1%) | 7887 (10.8%) |
| MAROON | 1786 (2.8%) | 260 (2.9%) | 2046 (2.8%) |
| NOT AVAIL | 70 (0.1%) | 24 (0.3%) | 94 (0.1%) |
| NULL | 7 (0.0%) | 1 (0.0%) | 8 (0.0%) |
| ORANGE | 381 (0.6%) | 34 (0.4%) | 415 (0.6%) |
| OTHER | 213 (0.3%) | 29 (0.3%) | 242 (0.3%) |
| PURPLE | 317 (0.5%) | 56 (0.6%) | 373 (0.5%) |
| RED | 5432 (8.5%) | 825 (9.2%) | 6257 (8.6%) |
| SILVER | 13032 (20.4%) | 1843 (20.5%) | 14875 (20.4%) |
| WHITE | 10617 (16.6%) | 1506 (16.8%) | 12123 (16.6%) |
| YELLOW | 210 (0.3%) | 34 (0.4%) | 244 (0.3%) |
| Transmission | |||
| NULL | 8 (0.0%) | 1 (0.0%) | 9 (0.0%) |
| AUTO | 61722 (96.4%) | 8676 (96.7%) | 70398 (96.5%) |
| MANUAL | 2277 (3.6%) | 299 (3.3%) | 2576 (3.5%) |
| WheelType | |||
| Alloy | 32065 (50.1%) | 3985 (44.4%) | 36050 (49.4%) |
| Covers | 30349 (47.4%) | 2655 (29.6%) | 33004 (45.2%) |
| NULL | 937 (1.5%) | 2237 (24.9%) | 3174 (4.3%) |
| Special | 656 (1.0%) | 99 (1.1%) | 755 (1.0%) |
| Nationality | |||
| AMERICAN | 53641 (83.8%) | 7387 (82.3%) | 61028 (83.6%) |
| NULL | 5 (0.0%) | 0 (0%) | 5 (0.0%) |
| OTHER | 168 (0.3%) | 27 (0.3%) | 195 (0.3%) |
| OTHER ASIAN | 6972 (10.9%) | 1061 (11.8%) | 8033 (11.0%) |
| TOP LINE ASIAN | 3221 (5.0%) | 501 (5.6%) | 3722 (5.1%) |
| Size | |||
| COMPACT | 6060 (9.5%) | 1145 (12.8%) | 7205 (9.9%) |
| CROSSOVER | 1576 (2.5%) | 183 (2.0%) | 1759 (2.4%) |
| LARGE | 8032 (12.5%) | 818 (9.1%) | 8850 (12.1%) |
| LARGE SUV | 1201 (1.9%) | 232 (2.6%) | 1433 (2.0%) |
| LARGE TRUCK | 2810 (4.4%) | 360 (4.0%) | 3170 (4.3%) |
| MEDIUM | 27244 (42.6%) | 3541 (39.4%) | 30785 (42.2%) |
| MEDIUM SUV | 6897 (10.8%) | 1193 (13.3%) | 8090 (11.1%) |
| NULL | 5 (0.0%) | 0 (0%) | 5 (0.0%) |
| SMALL SUV | 1963 (3.1%) | 313 (3.5%) | 2276 (3.1%) |
| SMALL TRUCK | 739 (1.2%) | 125 (1.4%) | 864 (1.2%) |
| SPECIALTY | 1739 (2.7%) | 176 (2.0%) | 1915 (2.6%) |
| SPORTS | 633 (1.0%) | 144 (1.6%) | 777 (1.1%) |
| VAN | 5108 (8.0%) | 746 (8.3%) | 5854 (8.0%) |
| TopThreeAmericanName | |||
| CHRYSLER | 20674 (32.3%) | 2725 (30.4%) | 23399 (32.1%) |
| FORD | 10389 (16.2%) | 1926 (21.5%) | 12315 (16.9%) |
| GM | 22578 (35.3%) | 2736 (30.5%) | 25314 (34.7%) |
| NULL | 5 (0.0%) | 0 (0%) | 5 (0.0%) |
| OTHER | 10361 (16.2%) | 1589 (17.7%) | 11950 (16.4%) |
| PRIMEUNIT | |||
| NO | 3230 (5.0%) | 127 (1.4%) | 3357 (4.6%) |
| NULL | 60721 (94.9%) | 8843 (98.5%) | 69564 (95.3%) |
| YES | 56 (0.1%) | 6 (0.1%) | 62 (0.1%) |
| AUCGUART | |||
| GREEN | 3215 (5.0%) | 125 (1.4%) | 3340 (4.6%) |
| NULL | 60721 (94.9%) | 8843 (98.5%) | 69564 (95.3%) |
| RED | 71 (0.1%) | 8 (0.1%) | 79 (0.1%) |
| VNST | |||
| AL | 602 (0.9%) | 88 (1.0%) | 690 (0.9%) |
| AR | 54 (0.1%) | 16 (0.2%) | 70 (0.1%) |
| AZ | 5470 (8.5%) | 704 (7.8%) | 6174 (8.5%) |
| CA | 6144 (9.6%) | 951 (10.6%) | 7095 (9.7%) |
| CO | 4394 (6.9%) | 604 (6.7%) | 4998 (6.8%) |
| FL | 9305 (14.5%) | 1142 (12.7%) | 10447 (14.3%) |
| GA | 2177 (3.4%) | 273 (3.0%) | 2450 (3.4%) |
| IA | 426 (0.7%) | 73 (0.8%) | 499 (0.7%) |
| ID | 177 (0.3%) | 19 (0.2%) | 196 (0.3%) |
| IL | 391 (0.6%) | 67 (0.7%) | 458 (0.6%) |
| IN | 418 (0.7%) | 68 (0.8%) | 486 (0.7%) |
| KY | 215 (0.3%) | 15 (0.2%) | 230 (0.3%) |
| LA | 297 (0.5%) | 52 (0.6%) | 349 (0.5%) |
| MA | 13 (0.0%) | 2 (0.0%) | 15 (0.0%) |
| MD | 993 (1.6%) | 165 (1.8%) | 1158 (1.6%) |
| MI | 13 (0.0%) | 1 (0.0%) | 14 (0.0%) |
| MN | 60 (0.1%) | 2 (0.0%) | 62 (0.1%) |
| MO | 676 (1.1%) | 82 (0.9%) | 758 (1.0%) |
| MS | 447 (0.7%) | 46 (0.5%) | 493 (0.7%) |
| NC | 6243 (9.8%) | 799 (8.9%) | 7042 (9.6%) |
| NE | 25 (0.0%) | 1 (0.0%) | 26 (0.0%) |
| NH | 88 (0.1%) | 9 (0.1%) | 97 (0.1%) |
| NJ | 277 (0.4%) | 40 (0.4%) | 317 (0.4%) |
| NM | 210 (0.3%) | 29 (0.3%) | 239 (0.3%) |
| NV | 472 (0.7%) | 90 (1.0%) | 562 (0.8%) |
| NY | 6 (0.0%) | 0 (0%) | 6 (0.0%) |
| OH | 728 (1.1%) | 67 (0.7%) | 795 (1.1%) |
| OK | 3263 (5.1%) | 331 (3.7%) | 3594 (4.9%) |
| OR | 198 (0.3%) | 13 (0.1%) | 211 (0.3%) |
| PA | 700 (1.1%) | 147 (1.6%) | 847 (1.2%) |
| SC | 3686 (5.8%) | 594 (6.6%) | 4280 (5.9%) |
| TN | 1561 (2.4%) | 203 (2.3%) | 1764 (2.4%) |
| TX | 11719 (18.3%) | 1877 (20.9%) | 13596 (18.6%) |
| UT | 769 (1.2%) | 106 (1.2%) | 875 (1.2%) |
| VA | 1399 (2.2%) | 263 (2.9%) | 1662 (2.3%) |
| WA | 128 (0.2%) | 8 (0.1%) | 136 (0.2%) |
| WV | 263 (0.4%) | 29 (0.3%) | 292 (0.4%) |
| IsOnlineSale | |||
| 0 | 62375 (97.5%) | 8763 (97.6%) | 71138 (97.5%) |
| 1 | 1632 (2.5%) | 213 (2.4%) | 1845 (2.5%) |
| VNZIP | |||
| 2764 | 13 (0.0%) | 2 (0.0%) | 15 (0.0%) |
| 3106 | 88 (0.1%) | 9 (0.1%) | 97 (0.1%) |
| 8505 | 277 (0.4%) | 40 (0.4%) | 317 (0.4%) |
| 12552 | 6 (0.0%) | 0 (0%) | 6 (0.0%) |
| 16066 | 6 (0.0%) | 0 (0%) | 6 (0.0%) |
| 16137 | 2 (0.0%) | 0 (0%) | 2 (0.0%) |
| 17028 | 33 (0.1%) | 0 (0%) | 33 (0.0%) |
| 17406 | 119 (0.2%) | 20 (0.2%) | 139 (0.2%) |
| 17545 | 100 (0.2%) | 36 (0.4%) | 136 (0.2%) |
| 19440 | 440 (0.7%) | 91 (1.0%) | 531 (0.7%) |
| 20166 | 458 (0.7%) | 111 (1.2%) | 569 (0.8%) |
| 21014 | 179 (0.3%) | 4 (0.0%) | 183 (0.3%) |
| 21075 | 814 (1.3%) | 161 (1.8%) | 975 (1.3%) |
| 22403 | 437 (0.7%) | 85 (0.9%) | 522 (0.7%) |
| 22801 | 467 (0.7%) | 66 (0.7%) | 533 (0.7%) |
| 23234 | 4 (0.0%) | 1 (0.0%) | 5 (0.0%) |
| 23606 | 33 (0.1%) | 0 (0%) | 33 (0.0%) |
| 25071 | 0 (0%) | 1 (0.0%) | 1 (0.0%) |
| 25177 | 142 (0.2%) | 13 (0.1%) | 155 (0.2%) |
| 26431 | 121 (0.2%) | 15 (0.2%) | 136 (0.2%) |
| 27407 | 483 (0.8%) | 91 (1.0%) | 574 (0.8%) |
| 27542 | 3072 (4.8%) | 330 (3.7%) | 3402 (4.7%) |
| 28273 | 1606 (2.5%) | 281 (3.1%) | 1887 (2.6%) |
| 28625 | 1082 (1.7%) | 97 (1.1%) | 1179 (1.6%) |
| 29070 | 247 (0.4%) | 18 (0.2%) | 265 (0.4%) |
| 29323 | 3 (0.0%) | 0 (0%) | 3 (0.0%) |
| 29461 | 285 (0.4%) | 53 (0.6%) | 338 (0.5%) |
| 29532 | 1472 (2.3%) | 203 (2.3%) | 1675 (2.3%) |
| 29697 | 1679 (2.6%) | 320 (3.6%) | 1999 (2.7%) |
| 30120 | 115 (0.2%) | 39 (0.4%) | 154 (0.2%) |
| 30212 | 526 (0.8%) | 69 (0.8%) | 595 (0.8%) |
| 30272 | 1043 (1.6%) | 120 (1.3%) | 1163 (1.6%) |
| 30315 | 120 (0.2%) | 9 (0.1%) | 129 (0.2%) |
| 30331 | 372 (0.6%) | 34 (0.4%) | 406 (0.6%) |
| 30529 | 1 (0.0%) | 2 (0.0%) | 3 (0.0%) |
| 32124 | 1045 (1.6%) | 117 (1.3%) | 1162 (1.6%) |
| 32219 | 590 (0.9%) | 104 (1.2%) | 694 (1.0%) |
| 32225 | 5 (0.0%) | 0 (0%) | 5 (0.0%) |
| 32503 | 29 (0.0%) | 5 (0.1%) | 34 (0.0%) |
| 32750 | 208 (0.3%) | 59 (0.7%) | 267 (0.4%) |
| 32772 | 69 (0.1%) | 16 (0.2%) | 85 (0.1%) |
| 32812 | 73 (0.1%) | 0 (0%) | 73 (0.1%) |
| 32824 | 3352 (5.2%) | 347 (3.9%) | 3699 (5.1%) |
| 33073 | 107 (0.2%) | 0 (0%) | 107 (0.1%) |
| 33311 | 114 (0.2%) | 22 (0.2%) | 136 (0.2%) |
| 33314 | 161 (0.3%) | 24 (0.3%) | 185 (0.3%) |
| 33411 | 237 (0.4%) | 36 (0.4%) | 273 (0.4%) |
| 33619 | 1558 (2.4%) | 181 (2.0%) | 1739 (2.4%) |
| 33762 | 148 (0.2%) | 28 (0.3%) | 176 (0.2%) |
| 33809 | 608 (0.9%) | 64 (0.7%) | 672 (0.9%) |
| 33916 | 26 (0.0%) | 8 (0.1%) | 34 (0.0%) |
| 34203 | 163 (0.3%) | 41 (0.5%) | 204 (0.3%) |
| 34761 | 812 (1.3%) | 90 (1.0%) | 902 (1.2%) |
| 35004 | 414 (0.6%) | 64 (0.7%) | 478 (0.7%) |
| 35613 | 188 (0.3%) | 24 (0.3%) | 212 (0.3%) |
| 37122 | 253 (0.4%) | 13 (0.1%) | 266 (0.4%) |
| 37138 | 17 (0.0%) | 1 (0.0%) | 18 (0.0%) |
| 37210 | 488 (0.8%) | 49 (0.5%) | 537 (0.7%) |
| 37421 | 245 (0.4%) | 3 (0.0%) | 248 (0.3%) |
| 37771 | 313 (0.5%) | 98 (1.1%) | 411 (0.6%) |
| 38118 | 230 (0.4%) | 39 (0.4%) | 269 (0.4%) |
| 38128 | 15 (0.0%) | 0 (0%) | 15 (0.0%) |
| 38637 | 199 (0.3%) | 25 (0.3%) | 224 (0.3%) |
| 39208 | 131 (0.2%) | 5 (0.1%) | 136 (0.2%) |
| 39402 | 117 (0.2%) | 16 (0.2%) | 133 (0.2%) |
| 42104 | 215 (0.3%) | 15 (0.2%) | 230 (0.3%) |
| 43207 | 11 (0.0%) | 1 (0.0%) | 12 (0.0%) |
| 45005 | 696 (1.1%) | 62 (0.7%) | 758 (1.0%) |
| 45011 | 21 (0.0%) | 4 (0.0%) | 25 (0.0%) |
| 46239 | 88 (0.1%) | 34 (0.4%) | 122 (0.2%) |
| 46803 | 122 (0.2%) | 8 (0.1%) | 130 (0.2%) |
| 47129 | 208 (0.3%) | 26 (0.3%) | 234 (0.3%) |
| 48265 | 13 (0.0%) | 1 (0.0%) | 14 (0.0%) |
| 50111 | 426 (0.7%) | 73 (0.8%) | 499 (0.7%) |
| 55369 | 60 (0.1%) | 2 (0.0%) | 62 (0.1%) |
| 60440 | 159 (0.2%) | 16 (0.2%) | 175 (0.2%) |
| 60443 | 103 (0.2%) | 14 (0.2%) | 117 (0.2%) |
| 60445 | 87 (0.1%) | 31 (0.3%) | 118 (0.2%) |
| 62207 | 42 (0.1%) | 6 (0.1%) | 48 (0.1%) |
| 63044 | 84 (0.1%) | 12 (0.1%) | 96 (0.1%) |
| 64153 | 9 (0.0%) | 1 (0.0%) | 10 (0.0%) |
| 64161 | 583 (0.9%) | 69 (0.8%) | 652 (0.9%) |
| 68138 | 25 (0.0%) | 1 (0.0%) | 26 (0.0%) |
| 70002 | 13 (0.0%) | 0 (0%) | 13 (0.0%) |
| 70401 | 91 (0.1%) | 16 (0.2%) | 107 (0.1%) |
| 70460 | 91 (0.1%) | 10 (0.1%) | 101 (0.1%) |
| 71119 | 102 (0.2%) | 26 (0.3%) | 128 (0.2%) |
| 72117 | 54 (0.1%) | 16 (0.2%) | 70 (0.1%) |
| 73108 | 1045 (1.6%) | 182 (2.0%) | 1227 (1.7%) |
| 73129 | 36 (0.1%) | 10 (0.1%) | 46 (0.1%) |
| 74135 | 2182 (3.4%) | 139 (1.5%) | 2321 (3.2%) |
| 75020 | 246 (0.4%) | 39 (0.4%) | 285 (0.4%) |
| 75050 | 1372 (2.1%) | 282 (3.1%) | 1654 (2.3%) |
| 75061 | 330 (0.5%) | 55 (0.6%) | 385 (0.5%) |
| 75236 | 2095 (3.3%) | 336 (3.7%) | 2431 (3.3%) |
| 76040 | 1404 (2.2%) | 201 (2.2%) | 1605 (2.2%) |
| 76063 | 324 (0.5%) | 21 (0.2%) | 345 (0.5%) |
| 76101 | 1 (0.0%) | 0 (0%) | 1 (0.0%) |
| 77041 | 1252 (2.0%) | 159 (1.8%) | 1411 (1.9%) |
| 77061 | 500 (0.8%) | 29 (0.3%) | 529 (0.7%) |
| 77073 | 111 (0.2%) | 25 (0.3%) | 136 (0.2%) |
| 77086 | 781 (1.2%) | 150 (1.7%) | 931 (1.3%) |
| 77301 | 8 (0.0%) | 1 (0.0%) | 9 (0.0%) |
| 78219 | 1036 (1.6%) | 137 (1.5%) | 1173 (1.6%) |
| 78227 | 753 (1.2%) | 163 (1.8%) | 916 (1.3%) |
| 78426 | 55 (0.1%) | 7 (0.1%) | 62 (0.1%) |
| 78610 | 72 (0.1%) | 12 (0.1%) | 84 (0.1%) |
| 78745 | 77 (0.1%) | 4 (0.0%) | 81 (0.1%) |
| 78754 | 1132 (1.8%) | 220 (2.5%) | 1352 (1.9%) |
| 79605 | 104 (0.2%) | 33 (0.4%) | 137 (0.2%) |
| 79932 | 66 (0.1%) | 3 (0.0%) | 69 (0.1%) |
| 80011 | 1154 (1.8%) | 98 (1.1%) | 1252 (1.7%) |
| 80022 | 1864 (2.9%) | 254 (2.8%) | 2118 (2.9%) |
| 80112 | 1 (0.0%) | 0 (0%) | 1 (0.0%) |
| 80229 | 596 (0.9%) | 120 (1.3%) | 716 (1.0%) |
| 80817 | 779 (1.2%) | 132 (1.5%) | 911 (1.2%) |
| 83687 | 14 (0.0%) | 0 (0%) | 14 (0.0%) |
| 83716 | 163 (0.3%) | 19 (0.2%) | 182 (0.2%) |
| 84087 | 154 (0.2%) | 11 (0.1%) | 165 (0.2%) |
| 84104 | 615 (1.0%) | 95 (1.1%) | 710 (1.0%) |
| 85009 | 712 (1.1%) | 128 (1.4%) | 840 (1.2%) |
| 85018 | 146 (0.2%) | 4 (0.0%) | 150 (0.2%) |
| 85040 | 1829 (2.9%) | 183 (2.0%) | 2012 (2.8%) |
| 85204 | 32 (0.0%) | 5 (0.1%) | 37 (0.1%) |
| 85226 | 1777 (2.8%) | 309 (3.4%) | 2086 (2.9%) |
| 85248 | 1 (0.0%) | 0 (0%) | 1 (0.0%) |
| 85260 | 0 (0%) | 1 (0.0%) | 1 (0.0%) |
| 85284 | 508 (0.8%) | 14 (0.2%) | 522 (0.7%) |
| 85338 | 1 (0.0%) | 0 (0%) | 1 (0.0%) |
| 85353 | 464 (0.7%) | 60 (0.7%) | 524 (0.7%) |
| 87105 | 208 (0.3%) | 29 (0.3%) | 237 (0.3%) |
| 87109 | 2 (0.0%) | 0 (0%) | 2 (0.0%) |
| 89120 | 105 (0.2%) | 23 (0.3%) | 128 (0.2%) |
| 89139 | 7 (0.0%) | 0 (0%) | 7 (0.0%) |
| 89165 | 353 (0.6%) | 67 (0.7%) | 420 (0.6%) |
| 89506 | 7 (0.0%) | 0 (0%) | 7 (0.0%) |
| 90045 | 234 (0.4%) | 38 (0.4%) | 272 (0.4%) |
| 90650 | 148 (0.2%) | 6 (0.1%) | 154 (0.2%) |
| 91752 | 970 (1.5%) | 205 (2.3%) | 1175 (1.6%) |
| 91763 | 32 (0.0%) | 3 (0.0%) | 35 (0.0%) |
| 91770 | 86 (0.1%) | 7 (0.1%) | 93 (0.1%) |
| 92057 | 265 (0.4%) | 47 (0.5%) | 312 (0.4%) |
| 92101 | 50 (0.1%) | 0 (0%) | 50 (0.1%) |
| 92337 | 635 (1.0%) | 176 (2.0%) | 811 (1.1%) |
| 92504 | 348 (0.5%) | 37 (0.4%) | 385 (0.5%) |
| 92807 | 922 (1.4%) | 164 (1.8%) | 1086 (1.5%) |
| 94544 | 696 (1.1%) | 56 (0.6%) | 752 (1.0%) |
| 95673 | 1758 (2.7%) | 212 (2.4%) | 1970 (2.7%) |
| 97060 | 63 (0.1%) | 1 (0.0%) | 64 (0.1%) |
| 97217 | 124 (0.2%) | 12 (0.1%) | 136 (0.2%) |
| 97402 | 11 (0.0%) | 0 (0%) | 11 (0.0%) |
| 98064 | 123 (0.2%) | 8 (0.1%) | 131 (0.2%) |
| 99224 | 5 (0.0%) | 0 (0%) | 5 (0.0%) |
Continous
| 0 (n=64007) |
1 (n=8976) |
Overall (n=72983) |
|
|---|---|---|---|
| VehOdo | |||
| Mean (SD) | 71000 (14600) | 74700 (14200) | 71500 (14600) |
| Median [Min, Max] | 72900 [5370, 114000] | 76500 [4830, 116000] | 73400 [4830, 116000] |
| MMRAcquisitionAuctionAveragePrice | |||
| Mean (SD) | 6230 (2420) | 5410 (2650) | 6130 (2460) |
| Median [Min, Max] | 6240 [0.00, 20300] | 5010 [0.00, 35700] | 6100 [0.00, 35700] |
| Missing | 17 (0.0%) | 1 (0.0%) | 18 (0.0%) |
| MMRAcquisitionAuctionCleanPrice | |||
| Mean (SD) | 7480 (2670) | 6630 (2940) | 7370 (2720) |
| Median [Min, Max] | 7450 [0.00, 24000] | 6200 [0.00, 36900] | 7300 [0.00, 36900] |
| Missing | 17 (0.0%) | 1 (0.0%) | 18 (0.0%) |
| MMRAcquisitionRetailAveragePrice | |||
| Mean (SD) | 8600 (3110) | 7760 (3350) | 8500 (3160) |
| Median [Min, Max] | 8570 [0.00, 24700] | 7450 [0.00, 39100] | 8440 [0.00, 39100] |
| Missing | 17 (0.0%) | 1 (0.0%) | 18 (0.0%) |
| MMRAcquisitionRetailCleanPrice | |||
| Mean (SD) | 9960 (3340) | 9090 (3620) | 9850 (3390) |
| Median [Min, Max] | 9920 [0.00, 27500] | 8830 [0.00, 41500] | 9790 [0.00, 41500] |
| Missing | 17 (0.0%) | 1 (0.0%) | 18 (0.0%) |
| MMRCurrentAuctionAveragePrice | |||
| Mean (SD) | 6230 (2390) | 5420 (2590) | 6130 (2430) |
| Median [Min, Max] | 6210 [0.00, 21800] | 5020 [0.00, 35700] | 6060 [0.00, 35700] |
| Missing | 283 (0.4%) | 32 (0.4%) | 315 (0.4%) |
| MMRCurrentAuctionCleanPrice | |||
| Mean (SD) | 7500 (2640) | 6640 (2880) | 7390 (2690) |
| Median [Min, Max] | 7450 [0.00, 25800] | 6210 [0.00, 36900] | 7310 [0.00, 36900] |
| Missing | 283 (0.4%) | 32 (0.4%) | 315 (0.4%) |
| MMRCurrentRetailAveragePrice | |||
| Mean (SD) | 8900 (3050) | 7920 (3270) | 8780 (3090) |
| Median [Min, Max] | 8890 [0.00, 24100] | 7620 [0.00, 39100] | 8730 [0.00, 39100] |
| Missing | 283 (0.4%) | 32 (0.4%) | 315 (0.4%) |
| MMRCurrentRetailCleanPrice | |||
| Mean (SD) | 10300 (3260) | 9260 (3520) | 10100 (3310) |
| Median [Min, Max] | 10300 [0.00, 28400] | 8980 [0.00, 41100] | 10100 [0.00, 41100] |
| Missing | 283 (0.4%) | 32 (0.4%) | 315 (0.4%) |
| VehBCost | |||
| Mean (SD) | 6800 (1710) | 6260 (2080) | 6730 (1770) |
| Median [Min, Max] | 6800 [1400, 16300] | 6000 [1.00, 45500] | 6700 [1.00, 45500] |
| WarrantyCost | |||
| Mean (SD) | 1260 (586) | 1360 (680) | 1280 (599) |
| Median [Min, Max] | 1160 [462, 7500] | 1240 [462, 6490] | 1160 [462, 7500] |
Plots of Categorical Variables
Make
## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數Color
## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數Size
## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數VNST
## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數VNZIP
## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.## Warning in L$marker$color[idx] <- aes2plotly(data, params, "fill")[idx]: 被
## 替換的項目不是替換值長度的倍數Auction
VehYear
VehicleAge
Transmission
WheelType
Nationality
TopThreeAmericanName
PRIMEUNIT
AUCGUART
IsOnlineSale
#Plots of Continuous Variables{.tabset .tabset-dropdown}
VehOdo
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
VehBCost
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
WarrantyCost
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
MMRAcquisitionAuctionAveragePrice
MMRAcquisitionAuctionCleanPrice
MMRAcquisitionRetailAveragePrice
MMRAcquisitionRetailCleanPrice
MMRCurrentAuctionAveragePrice
MMRCurrentAuctionCleanPrice
MMRCurrentRetailAveragePrice
MMRCurrentRetailCleanPrice
